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Development of Threshold Algorithms for a Location Problem with Elastic Demand

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Large-Scale Scientific Computing (LSSC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10665))

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Abstract

This work is devoted to development of threshold algorithms for one static probabilistic competitive facility location and design problem in the following formulation. New Company plans to enter the market and to locate new facilities with different design scenarios. Clients of each point choose to use the facilities of Company or its competitors depending on their attractiveness and distance. The aim of the new Company is to capture the greatest number of customers thus serving the largest share of the demand. This share for the Company is elastic and depends on clients’ decisions. We offer three types of threshold algorithms: Simulated annealing, Threshold improvement and Iterative improvement. Experimental tuning of parameters of algorithms was carried out. A comparative analysis of the algorithms, depending on the nature and value of the threshold on special test examples up to 300 locations is carried out. The results of numerical experiments are discussed.

T. Levanova—This research was supported by the Program of Fundamental Scientific Research of the State Academies of Sciences, task I.5.1.6.

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Authors and Affiliations

  1. Omsk Branch, Sobolev Institute of Mathematics, Pevtsova str. 13, 644043, Omsk, Russia

    Tatyana Levanova & Alexander Gnusarev

  2. Dostoevsky Omsk State University, Prospekt Mira 55A, 644077, Omsk, Russia

    Tatyana Levanova

Authors
  1. Tatyana Levanova
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  2. Alexander Gnusarev
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Correspondence to Tatyana Levanova .

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Editors and Affiliations

  1. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Ivan Lirkov

  2. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Svetozar Margenov

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Levanova, T., Gnusarev, A. (2018). Development of Threshold Algorithms for a Location Problem with Elastic Demand. In: Lirkov, I., Margenov, S. (eds) Large-Scale Scientific Computing. LSSC 2017. Lecture Notes in Computer Science(), vol 10665. Springer, Cham. https://doi.org/10.1007/978-3-319-73441-5_41

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  • DOI: https://doi.org/10.1007/978-3-319-73441-5_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73440-8

  • Online ISBN: 978-3-319-73441-5

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